forum.smartpls.com

Dear PLS users/experts/developers,

Could someone please help with the computation of f-square and q-squared. As far as Prof Hair's book can tell, both of these effect sizes should be computed manually by excluding predecessors for target latent variables and check any changes in R squared and Q squared.

1). In smartpls 3.2.4 the f square values were included in the bootstrap report (are these the f square that once should be computed manually if using smartpls 2 ?)

2). While smartpls 3.2.4 provided the f square values in bootstrapping report, it doesn't provide the q square values in blindfolding report. So correct me if I am wrong, for smartpls 3.2.4 the f square values can directly be generated from bootstrapping report, while the q square values still need to be computed manually by Prof Hair's formula?

3). For the q square values manual computation, do we delete the path arrow of the predecessor LV to target LV or delete the entire predecessor LV in order to compute the changes in Q square (i.e. for q square computation) and/or R square (for f square computation) ?

Lots of thanks forum members,
Ferry

Bumping up this question. Would love to hear an answer, too.
Thank you

1) Yes, you do not have to calculate them manually in SmartPLS 3.

2) Yes, while we provide the f-square, you still need to calculate the q-square manually.

3) This is a good question. It is not yet decided on how to do it correctly for Blindfolding. That is why we have not implemented it in SmartPLS 3 so far. You should make your decisions explicit (write them into your manuscript) when you report the q-square, so that reviewers can evaluate them.

Dear PLS experts,

In SmartPLS 3.2.4,

1. Q² (=1-SSE/SSO) from Construct Crossvalidated Redundancy from blindfolding. Is this not the predictive relevance q square values? If not, then how do we manually calculate q square?

2. For f square, from bootstrapping results there is Original Sample (O),Sample Mean (M),Standard Deviation (STDEV),T Statistics (|O/STDEV|),P Values, which one is actually the f square values?

Looking forward for your expert advises.

Regards

zameer,
the f square values can be found after calculating the PLS algorithm and looking at the results of it, then clicking on the "f square" link on the bottom right of your program. (for PLS 3.2.4)

Best

The large Q² is the predictive relevance of the structural model for predicting the indicators of an endogenous constructs.
The small q² is a quasi-effect size measure of the difference in Q² after including and excluding a certain predictor construct from the model. However, we do not automatically report the q² effect size as there are some unsolved conceptual questions that need expert judgment when calculating the results.

Dear Experts
I have gone through Cohen 1992 power table, and also the sample size calculation based on the table in book on Premiers in PLS
Cohen 1992 look at effect sizes (small, large, medium). The book on Premier in PLS looks at min R square

I am working on a model (all reflective constructs)

A->B
A->C
A->D

B->E
C->E
D->E

E is the final endogenous construct
I calculated the effect size using the steps mentioned in the PLS SEM facebook page "f² Effect Size Computation - How to Do it in SmartPLS"
f² effect size = (R²(incl.) – R²(excl.)) / (1 - R²(incl.));

The effect sizes are
for B->E is 0.64
for C->E is 0.034
for D->E is 0.02

Kindly help me with these questions
What does minimum R squared mean? ( referred from Pg 21 table on how to select sample from book - Premier on PLS)
for my model
R squared included = 0.6306
R squared B excluded =0.393
R squared C excluded = 0.618
Rsquared D excluded = 0.623

I am confused. How to calculate the sample size from all this information
On what basis should I select the min R squared (0.1, 0.25, 0.5, 0.75)
I really need your help to progress.Kindly do advise as cohen paper has no direct reference to R squared and talks about small, medium and large effect sizes only.

Kindly do help.
Regards
Avinash

please help us about computing q-squared by PLS 3 manually.
I mean the Q2 formula by pls 3 .
thank you advanced

q² = (Q²_included - Q²_excluded) / (1 - Q²_included)

Hello!

So, my structural model has one exogenous (IQ) and two endogenous variables (XV and TS). Hair et al. mention that in order to determine the q-square, we have to delete "a specific predecessor of that endogenous latent variable." Therefore, by deleting IQ I got IQ on TS, and by deleting XV I got EV on TS, however, is there something I should do to get IQ on XV? Am I missing something? Thank you!

I don't fully understand the description of your model. Anyways, if you only have one explanatory for an endogenous latent variable, the q² computation does not work - or you could say that Q²_excluded is 0 and, then, plug in the value of Q²_included and 0 for Q² excluded into the equation: q² = (Q²_included - Q²_excluded) / (1 - Q²_included)


Best
CR